As the highest-ranking public research library in the U.S., the University Library at Berkeley provides the intellectual resources to support the University's diverse teaching and research activities. It has enabled generations of Cal scholars to teach and learn, to reflect on the past and shape the future, and to advance human understanding and knowledge.
This case study recounts a process of course design, conduct, and evaluation for a single-session chemical information literacy class using guided and team-based learning. This approach incorporates active learning, worked examples, process worksheets, and POGIL elements. The instruction followed an iterative cycle of learning exercises whereby (1) the instructor introduces an information problem or task through a short presentation, (2) student teams collaboratively work through process worksheets that guide them through the technical and analytical tasks of resolving the information problem or task, (3) the instructor serves as a facilitator to address learning needs that arise during the exercise, while student teams analyze and reflect upon the learning activity and concepts, and afterwards, (4) the class engages in a discussion as an opportunity for evaluation, further exploration, and peer instruction. Overall, the guided and team-based learning approach offers opportunities to observe student progress closely and forges a collaborative spirit between students and the instructor for an engaging and rewarding experience.
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Big Data for Big Questions: Assessing the Impact of Non-English Language Sources on Doctoral Research at Berkeley
Even the largest research library can no longer build comprehensive collections from all countries and in all languages. The pressure to justify acquisitions can be great on non-English language materials, which are often low-use in North American universities. Determining the research need for these materials, and assessing how well it is being met, is challenging. This paper analyzes impact by examining the language of cited references in doctoral dissertations at Berkeley, 2008-2015.
The purpose of this study was to characterize a representative body of research to demonstrate the advantage of disseminating educational research in ways that reach the broadest audience. Using the Education Resources Information Center (ERIC) database, I compiled a set of research findings on a number of broad educational themes. Focusing on journal articles and reports, I examined the public availability of the publications, publication quality as determined by peer review, and authorship. In all, 65% of the journal articles were behind a paywall, and 35% were available either as PDFs or freely available on the publisher website; 61% of the peer-reviewed literature was locked behind a paywall. This study also examined a subset of reports—research studies not published in journals but issued by organizations, think tanks, or policy institutes; 27% of the reports were authored by institutions identified with a neoliberal or free-market ideology
Swanson’s thesis drew on an extensive and varied array of sources on the growth of wind power on China: Chinese language article databases and government statistics, international energy statistics databases, newspapers, journal articles, policy papers, even social media tools used by energy analysts. His attempt to explain how technical and political challenges have affected China’s wind power growth resulted in what Prof. Bhandari described as “simply the best thesis that I have read in ISF.” Even Swanson recognized this achievement, stating that writing this thesis “marked the turning point in my education when I began to produce knowledge, not just consume and regurgitate knowledge.”
Opportunities and Constraints in Characterizing Landscape Distribution of an Invasive Grass from Very High Resolution Multi-Spectral Imagery
Understanding spatial distributions of invasive plant species at early infestation stages is critical for assessing the dynamics and underlying factors of invasions. Recent progress in very high resolution remote sensing is facilitating this task by providing high spatial detail over whole-site extents that are prohibitive to comprehensive ground surveys. This study assessed the opportunities and constraints to characterize landscape distribution of the invasive grass medusahead (Elymus caput-medusae) in a ∼36.8 ha grassland in California, United States from 0.15m-resolution visible/near-infrared aerial imagery at the stage of late spring phenological contrast with dominant grasses. We compared several object-based unsupervised, single-run supervised and hierarchical approaches to classify medusahead using spectral, textural, and contextual variables. Fuzzy accuracy assessment indicated that 44–100% of test medusahead samples were matched by its classified extents from different methods, while 63–83% of test samples classified as medusahead had this class as an acceptable candidate. Main sources of error included spectral similarity between medusahead and other green species and mixing of medusahead with other vegetation at variable densities. Adding texture attributes to spectral variables increased the accuracy of most classification methods, corroborating the informative value of local patterns under limited spectral data. The highest accuracy across different metrics was shown by the supervised single-run support vector machine with seven vegetation classes and Bayesian algorithms with three vegetation classes; however, their medusahead allocations showed some “spillover” effects due to misclassifications with other green vegetation. This issue was addressed by more complex hierarchical approaches, though their final accuracy did not exceed the best single-run methods. However, the comparison of classified medusahead extents with field segments of its patches overlapping with survey transects indicated that most methods tended to miss and/or over-estimate the length of the smallest patches and under-estimate the largest ones due to classification errors. Overall, the study outcomes support the potential of cost-effective, very high-resolution sensing for the site-scale detection of infestation hotspots that can be customized to plant phenological schedules. However, more accurate medusahead patch delineation in mixed-cover grasslands would benefit from testing hyperspectral data and using our study’s framework to inform and constrain the candidate vegetation classes in heterogeneous locations.
De novo characterization of the gene-rich transcriptomes of two color-polymorphic spiders, Theridion grallator and T. californicum (Araneae: Theridiidae), with special reference to pigment genes
A number of spider species within the family Theridiidae exhibit a dramatic abdominal (opisthosomal) color polymorphism. The polymorphism is inherited in a broadly Mendelian fashion and in some species consists of dozens of discrete morphs that are convergent across taxa and populations. Few genomic resources exist for spiders. Here, as a first necessary step towards identifying the genetic basis for this trait we present the near complete transcriptomes of two species: the Hawaiian happy-face spider Theridion grallator and Theridion californicum. We mined the gene complement for pigment-pathway genes and examined differential expression (DE) between morphs that are unpatterned (plain yellow) and patterned (yellow with superimposed patches of red, white or very dark brown).
By deep sequencing both RNA-seq and normalized cDNA libraries from pooled specimens of each species we were able to assemble a comprehensive gene set for both species that we estimate to be 98-99% complete. It is likely that these species express more than 20,000 protein-coding genes, perhaps 4.5% (ca. 870) of which might be unique to spiders. Mining for pigment-associated Drosophila melanogaster genes indicated the presence of all ommochrome pathway genes and most pteridine pathway genes and DE analyses further indicate a possible role for the pteridine pathway in theridiid color patterning.
Based upon our estimates, T. grallator and T. californicum express a large inventory of protein-coding genes. Our comprehensive assembly illustrates the continuing value of sequencing normalized cDNA libraries in addition to RNA-seq in order to generate a reference transcriptome for non-model species. The identification of pteridine-related genes and their possible involvement in color patterning is a novel finding in spiders and one that suggests a biochemical link between guanine deposits and the pigments exhibited by these species.
Biochar may contribute to climate change mitigation at negative cost by sequestering photosynthetically fixed carbon in soil while increasing crop yields. The magnitude of biochar's potential in this regard will depend on crop yield benefits, which have not been well-characterized across different soils and biochars. Using data from 84 studies, we employ meta-analytical, missing data, and semiparametric statistical methods to explain heterogeneity in crop yield responses across different soils, biochars, and agricultural management factors, and then estimate potential changes in yield across different soil environments globally. We find that soil cation exchange capacity and organic carbon were strong predictors of yield response, with low cation exchange and low carbon associated with positive response. We also find that yield response increases over time since initial application, compared to non-biochar controls. High reported soil clay content and low soil pH were weaker predictors of higher yield response. No biochar parameters in our dataset—biochar pH, percentage carbon content, or temperature of pyrolysis—were significant predictors of yield impacts. Projecting our fitted model onto a global soil database, we find the largest potential increases in areas with highly weathered soils, such as those characterizing much of the humid tropics. Richer soils characterizing much of the world's important agricultural areas appear to be less likely to benefit from biochar.